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X. Lagorce and R. Benosman
Figure 1: A network generating a constant value when required. When the
recall neuron is activated, the output neuron will generate a pair of spikes
coding for value x.
This representation allows us to encode any value between a minimum
and a maximum interspike (of Tmin and Tmax = (Tmin + Tcod )). We chose to
use a minimum interspike to encode zero for several reasons. If the two
spikes encoding a value originate from one unique neuron or are received
by a single neuron, this minimum interspike provides time to recover from
the first spike before spiking again. Tmin allows networks to react to the first
input spike and propagate a state change before the second encoding spike
is received. In the experiments presented in this work, we use Tmin = 10 ms
and Tcod = 100 ms.
We could also choose a logarithmic function to allow encoding a large
range of values with dynamic precision (precision would be smaller for
large values).
To represent signed values, we use two different pathways for the two
different signs. Positive values will be encoded by causing a neuron to spike
and negative values by eliciting another neuron to spike. We arbitrarily
chose to represent zero as a positive value.
To ease the understanding and routing of several networks, each implementing simple operations, we add some interface neurons to these
networks. For instance, the input of the networks is materialized by special input neurons and their output by some output, output + or output −
neurons. Other special neurons used for interaction between networks are
also marked. In all the figures describing networks, neurons in blue are
input neurons to the networks and neurons in red are output neurons of
the circuit.
The simple network presented in Figure 1 encodes a constant value. It
shows the design principles used in the rest of this section. In the network
shown in Figure 1, the recall neuron is an input. When a spike is received
by recall, the constant value encoded in the network is output to the output
neuron. In this example, the output is generated by two different synaptic
connections from recall to output. They generate two output spikes with the
interspike corresponding to the encoded value.